# A Fully Convolutional Neural Network based Structured Prediction Approach Towards the Retinal Vessel Segmentation Source: [A Fully Convolutional Neural Network based Structured Prediction Approach Towards the Retinal Vessel Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:30:18.740Z Authors: Avijit Dasgupta, Sonam Singh Published: 2016-11-07T14:16:18.000Z ## Abstract Automatic segmentation of retinal blood vessels from fundus images plays an important role in the computer aided diagnosis of retinal diseases. The task of blood vessel segmentation is challenging due to the extreme variations in morphology of the vessels against noisy background. In this paper, we formulate the segmentation task as a multi-label inference task and utilize the implicit advantages of the combination of convolutional neural networks and structured prediction. Our proposed convolutional neural network based model achieves strong performance and significantly outperforms the state-of-the-art for automatic retinal blood vessel segmentation on DRIVE dataset with 95.33% accuracy and 0.974 AUC score. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Retinal vessel segmentation on SYNTHE](): Retinal Vessel Segmentation; SYNTHE; PPV: 0.8485. - [Retinal Vessel Segmentation on DRIVE (test)](): Retinal Vessel Segmentation; DRIVE (test); Accuracy: 95.33. ## Original sources and implementation [Original paper]() [PDF]()